{"paper":{"title":"DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"DUET predicts spatial gene expression from histology images by combining regression and retrieval under single-cell constraints.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongyu Qu, Haichun Yang, Juming Xiong, Junchao Zhu, Junlin Guo, Marilyn Lionts, Ruining Deng, Shilin Zhao, Tianyuan Yao, Yanfan Zhu, Yuankai Huo, Yuechen Yang, Yu Wang, Zhengyi Lu","submitted_at":"2026-05-13T20:43:29Z","abstract_excerpt":"Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing methods reduce this task to a simple morphology-to-expression mapping, where visual similarity does not guarantee molecular consistency. Meanwhile, single-cell data has amassed rich resources far surpassing the scale of ST data, yet it remains underexplored in vision-omics modeling. Furthermore, current approaches commit to a monolithic paradigm with bottlenecks, unable to balance expressive flexibility with biological fidelity. To bridge thes"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"DUET achieves SOTA performance, with consistent gains contributed by each proposed component.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That large-scale single-cell references can reliably impose molecular states as biological constraints to mitigate aleatoric vision ambiguity in histology images.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DUET adaptively merges parametric prediction and single-cell retrieval to achieve state-of-the-art inference of spatial gene expression from histology images.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DUET predicts spatial gene expression from histology images by combining regression and retrieval under single-cell constraints.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"39fd5939410f2b304c449e6fff6031c51889b4067668790202e8bc4d334148a4"},"source":{"id":"2605.14104","kind":"arxiv","version":1},"verdict":{"id":"892b9c5f-15b5-44ba-b720-586610642962","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:21:45.989739Z","strongest_claim":"DUET achieves SOTA performance, with consistent gains contributed by each proposed component.","one_line_summary":"DUET adaptively merges parametric prediction and single-cell retrieval to achieve state-of-the-art inference of spatial gene expression from histology images.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That large-scale single-cell references can reliably impose molecular states as biological constraints to mitigate aleatoric vision ambiguity in histology images.","pith_extraction_headline":"DUET predicts spatial gene expression from histology images by combining regression and retrieval under single-cell constraints."},"references":{"count":40,"sample":[{"doi":"","year":2019,"title":"Spatial transcriptomics coming of age.Nature Reviews Genetics, 20(6):317–317, 2019","work_id":"b3c403b4-a5f9-489d-996b-c7e6e25b53e3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart.Cell, 179(7):1647–1660, 2019","work_id":"94c15420-c436-4a8b-bebf-4edbad401065","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Spatially re- solved transcriptomes—next generation tools for tissue exploration.Bioessays, 42(10):1900221, 2020","work_id":"30555e8b-d73e-432e-a1b6-71e8dc1d928d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Advances and challenges in spatial transcriptomics for developmental biology.Biomolecules, 13(1):156, 2023","work_id":"63e19b4d-b097-4a20-90a0-9c32c12619d1","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Advances in spatial transcriptomics and its applications in cancer research.Molecular Cancer, 23(1):129, 2024","work_id":"02b03f44-a59c-471a-8ef5-4841df28dc75","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":40,"snapshot_sha256":"da240de2e8feab37fcf5d8e7d1c6568214aecf0ef3a678030407019cbf809c90","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}